Abstract
Recent advances in high-throughput sequencing techniques have made it possible to tag ribonucleoside monophosphates (rNMPs) embedded in genomic DNA for sequencing. rNMP sequencing experiments generate large, complex datasets that require efficient, scalable software that can accurately map embedded rNMPs independently of the particular sequencing technique used. Current computational pipelines designed to map rNMPs embedded in genomic DNA are customized for data generated using only one type of rNMP sequencing technique. To standardize the processing and analysis of rNMP sequencing experiments, we developed Ribose-Map. Through a series of analytical modules, Ribose-Map transforms raw sequencing data into summary datasets and publication-ready visualizations of results, allowing biologists to identify sites of embedded rNMPs, study the nucleotide sequence context of these rNMPs and explore their genome-wide distribution. By accommodating data from any of the available rNMP sequencing techniques, Ribose-Map can increase the reproducibility of rNMP sequencing experiments and enable a head-to-head comparison of these experiments.
Highlights
Ribonucleoside monophosphates, the subunits of RNA, are the most common non-canonical nucleotides embedded in genomic DNA
Embedded Ribonucleoside monophosphates (rNMPs) are the most frequent distortions found in genomic DNA, there is still much to be learned about the biological mechanisms that regulate the presence of embedded rNMPs in DNA and the effects of embedded rNMPs on genome stability, DNA metabolism and disease
To understand the relevance of rNMPs embedded in genomic DNA, it is critical to first know where embedded rNMPs are located in the genome
Summary
Ribonucleoside monophosphates (rNMPs), the subunits of RNA, are the most common non-canonical nucleotides embedded in genomic DNA. In addition to using alkali to cleave at the 3 side of embedded rNMPs, ribose-seq takes advantage of Arabidopsis thaliana tRNA ligase to directly capture rNMPs along with the nucleotides upstream from them, placing the rNMPs as the reverse complements of the tagged nucleotides. Each sequencing technique is unique in how it tags embedded rNMPs, the overall goal remains the same: to map the genome-wide distribution of embedded rNMPs to single-nucleotide resolution. Together, these techniques will help researchers investigate the biological mechanisms that regulate the presence of embedded rNMPs and the effects of embedded rNMPs on genome stability, DNA metabolism and disease
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